System for reclassification of electronic messages in a spam filtering system

ABSTRACT

A method for indicating probability of spam for email comprises tracking network traffic characteristics for the email, and comparing the tracked characteristics for the email to characteristics for email from trusted or known spam sources.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/882,714, filed Jun. 30, 2004, which is incorporated by referenceherein in its entirety.

FIELD OF THE INVENTION

The present invention is in the field of communication services, andapplies more particularly to methods and apparatus for filteringmessages for Spam.

BACKGROUND OF THE INVENTION

In the art of filtering email for Spam messages, one standard tool thatis commonly used is a compiled data list defining user-approved contactemail addresses, which is a list commonly known in the art as awhitelist. A whitelist is a list of user contacts typically taken from auser's email address book and used to validate incoming email bycomparing the sender address of the email to the addresses in the list.For example, if an email arrives for the user and the sender address isfound in the user's whitelist of trusted contacts, then that particularemail is typically allowed through to the user's inbox. If the sender'saddress is not in the list then the user may be alerted of possibleSpam.

It is common in the art to use whitelists that are manually constructedor built from a user's address book. Whitelists, however, may bedifficult to build and maintain. If a user is not diligent inmaintaining a comprehensive address book, for example, the associatedfiltering system may not have a comprehensive list and as a result maynot make good decisions.

One drawback to current whitelisting techniques is that they may berelatively inflexible in terms of contact identification. For example,in collaboration, a trusted contact might send an email that alsoidentifies other trusted contacts through such as carbon copy (CC) andblind carbon copy (BCC) identification. However, if the user does notphysically add those trusted contacts to his or her address book, thenan email sent to the user from one of the trusted contacts may not getthrough to the user because it is not on the whitelist. Whitelists mayalso be inflexible in that modification (adding or deleting contacts)often is largely a manual process involving much work for a user. Whilesome effort at automation in building whitelists has occurred in theart, that effort has typically fallen short of a goal of flexibility, asit typically involves use of keys or tags that may inadvertently beattributed to undesirable sender addresses. Additionally if a contactformerly trusted becomes a distrusted contact, the contact is typicallymanually removed or blocked.

Further to the above, careful observers, using tools developed for thepurpose, have developed considerable knowledge of techniques used byspammers. Spam campaigns, and characteristics of their operation aretracked and recorded, and used in efforts to block spam. At the sametime, spammers study the new tools and techniques used to block theirefforts, and try to develop new and better techniques for overcoming theobstacles placed in their paths. Among the characteristics of spamcampaigns are certain traffic characteristics that indicate apossibility that email campaigns may be spam.

Therefore, what is clearly needed in the art is a method and apparatusproviding probability that certain emails or email campaigns may bespam, and used for such developed probabilities, such as from networktraffic characteristics.

SUMMARY OF THE INVENTION

In an embodiment of the invention a method for indicating probability ofspam for email is provided, comprising tracking network trafficcharacteristics for the email, and comparing the tracked characteristicsfor the email to characteristics for email from trusted or known spamsources. In some embodiments of the method the characteristics mayinclude one or more of traffic volume, burstiness, number of recipients,number of purported senders, or mail recipient connection type. Trackedvalues may be compared with known values for trusted or distrustedsources to provide rating values for indicating trustworthiness of thetracked email.

In some embodiments the method may include an act for combining ratingvalues associated with more than one characteristic to provide a singlecombined rating value. There may also be an act for using the ratingvalue or values to trigger mail sorting functions. The mail sortingfunctions may include one or more of diverting emails for furthertesting, or destroying the emails.

In another aspect of the invention a system for filtering emails isprovided, comprising a network-connected server, and intelligenceoperable on the server for tracking traffic-related characteristics foremail and comparing tracked characteristics to characteristics for emailfrom trusted or known spam sources. In some embodiments of the systemthe characteristics may include one or more of traffic volume,burstiness, number of recipients, number of purported senders, or mailrecipient connection type. Tracked values may be compared with knownvalues for trusted or distrusted sources to provide rating values forindicating trustworthiness of the tracked email.

In some embodiments the system may include an act for combining ratingvalues associated with more than one characteristic to provide a singlecombined rating value. Also in some embodiments there may be an act forusing the rating value or values to trigger mail sorting functions. Themail sorting functions may include one or more of diverting emails forfurther testing, or destroying the emails.

In yet another embodiment of the invention a machine readable mediumhaving stored thereon a set of instructions that cause a machine toperform a method is provided, the method comprising tracking networktraffic characteristics for the email; and comparing the trackedcharacteristics for the email to characteristics for email from trustedor known spam sources.

In some embodiments the characteristics may include one or more oftraffic volume, burstiness, number of recipients, number of purportedsenders, or mail recipient connection type. Tracked values may becompared with known values for trusted or distrusted sources to providerating values for indicating trustworthiness of the tracked email.

In some embodiments the method may include an act for combining ratingvalues associated with more than one characteristic to provide a singlecombined rating value. In other embodiments the method may include anact for using the rating value or values to trigger mail sortingfunctions. Further, the mail sorting functions may include one or moreof diverting emails for further testing, or destroying the emails.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating a whitelist filteringarchitecture according to prior-art.

FIG. 2 is a block diagram illustrating a Spam-filtering architecture inaccordance with one embodiment of the present invention.

FIG. 3 is a relationship diagram illustrating an exemplary relationshiphierarchy between a user and a sender in accordance with one embodimentof the present invention.

FIG. 4 is a table of possible communication activity conditions thatmight exist and be displayed in a template or list of statistics inaccordance with one embodiment of the present invention.

FIG. 5 is a flow diagram illustrating basic activities or acts of aprocessor of FIG. 1 according to one embodiment of the presentinvention.

FIG. 6 is a process flow chart illustrating basic acts involved inmessage filtering including data updating in accordance with oneembodiment of the present invention.

FIG. 7 is a block diagram illustrating an associative comparison of useraccounts maintained in a data store for any commonality according to oneembodiment of the present invention.

FIG. 8 is a table of network traffic activity properties in accordancewith one embodiment of the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

In accordance with one embodiment of the present invention, the inventorprovides a system for runtime classification of Spam sent in email basedon evaluation of user account activity history. The methods andapparatus of the invention are described below in enabling detailaccording to various embodiments.

FIG. 1 is a block diagram illustrating a whitelist filteringarchitecture according to prior-art. As was described in the backgroundsection of this specification, whitelist filtering, as it relates toemail, typically involves logically or physically blocking any emailsaddressed to a particular user if the listed address or sender addressof the email is not listed in a whitelist of trusted contact addresses.

The prior-art architecture illustrated in FIG. 1 shows whitelistfiltering between a user station 107 and an email server 101 using awhitelist 104. User station 107 may be a computer system that accessesemail server 101 by physically connecting to the server over theInternet network for example. Internet connectivity between user station107 and server 101 is illustrated herein by an Internet backbone 108with connection to email server ports 102 from user station 107. Alsotypical is the use of an email client 105 on user station 107 to accessthe user account on server 101 for email interaction. In some cases theemail account of the user operating station 107 might be Web-basedwherein the user does not require an email client application in orderto interact.

In the prior art ports 102 may include a Post Office Protocol (POP3)port 110, a Simple Message Transport Protocol (SMTP) port 25, and anInstant Message Access Protocol (IMAP) port 143. Port 110 may allowaccess for retrieving and viewing email using a desktop client 105, forexample. Port 25 may allow outgoing email to be distributed todestinations outside of server 101, such as to other email servershosted by other service providers. Port 143 may allow clientlessinteraction from any network-capable machine and the user interface maybe a Web page. Typically, an IMAP-enhanced server may allow accessthrough many browser applications through an ISP account. Using IMAPalso may enable users to access email services from a variety ofnetwork-capable appliances. Email server 101, as is typical of prior-artservers, may include a message store and a send queue 105. Message store103 may typically be a text list containing separate account headingsfor each subscribing user, under which user messages being stored at theserver may be listed. A user for the purpose of viewing and downloadingemail typically may access store 103. In a store and forward embodiment,store 103 may be used to store all of a user's incoming email, generallylisted sequentially under the user account header. Attachments thatmight be sent along with messages are in many cases tagged to themessages and stored separately for download.

Send queue 105 is typically where messages are stored for send fromserver 101 to other server destinations. Any created outgoing mail maybe deposited in queue 105 and sent out to other server destinations or,in some cases, to message store 103 if the intended recipient is aclient of server 101. In this case one may assume a data connectionbetween queue 105 and store 103 although none is illustrated.

A user operating station 107 may have a whitelist 104 associated withhis or her email account with the host of server 101. Whitelist 104typically contains identification of any contacts that the user trustsfor email correspondence. In many cases a whitelist is compiled from auser's email address book associated with client 105. Whitelist 104 maybe illustrated both at the location of station 107 and at the locationof server 104 as part of user data pool 106. Data pool 106 typically maycontain all of the user whitelist data for all of the subscribers ofemail server 101. Whitelist 104 may represent just one list attributedto a single user.

In a typical case whitelist 104 may be available to both station 107 andto server 101 so that a user may update first 104 and then synchronizeit with the list held at server 101. In typical operation, server 101may process incoming email for the user operating station 107 and client105 against, hopefully, a current version of list 104 accessed by theserver from user data 106 at the time of email processing. Basically itmay be a sorting operation wherein any emails that do not have a “from”email address that matches an email address found on list 104 may beidentified and tagged. Depending on the exact scheme, tagged messagesmay be labeled Spam and sorted at the time of viewing or downloaded intoa junk mail folder or an equivalent set up on the user's client 105.While some optimization may exist for automated updating orsynchronization of the user's data, it may be largely the responsibilityof the user to maintain an active and current list 104. The user mayhave to add contacts that are trusted, delete contacts that are nolonger used or trusted and so on. The task of whitelist maintenance mayoften become an onerous task for the user and the inflexibility of thesystem should be readily apparent to any one with skill in the art ofwhitelisting technologies.

FIG. 2 is a block diagram illustrating a Spam filtering architecture inaccordance with one embodiment of the present invention. Some of theelements of this example are also present in the prior-art example ofFIG. 1 above but may exist unmodified by the present invention. Suchcomponents present in both FIG. 1 and of FIG. 2, but unchanged inembodiments of the present invention, retain their original elementnumbers and are not reintroduced. The term Spam is defined for thepresent purpose as any incoming message from any sender that might beconsidered undesirable or not wanted to a user, including commercialunsolicited email and private unsolicited communication, among others.

In this example user 107 may use the same email client 105 and mayaccess email using the same connection network 108 in the way describedwith reference FIG. 1 above. An email server 201 in this embodiment is,in the present example, enhanced for practice of the invention.

Email server 201 typically has ports 102, message store 103, and sendqueue 105, which may be unchanged from the description provided relativeto prior art above. A user operating client 105 may access server 201using ports 102 in the same basic fashion as described in the prior-artexample above. An enhancement to server 201 in one embodiment mayinclude provision of a filtering processor 206 that may be adapted amongother functions to access user communication history data to determinewhether the user will accept any particular emails addressed to him orher as legitimate emails.

Processor 206 can be a part of the processing resource of server 201 orof a separate and dedicated processor that might be built into ordefined separately from server 201. Processor 206, in one embodiment,may include all of the required functions enabled by logic or softwarethat may be required to classify a message. These functions may includebut should not be limited to, an email address feature extractor orparsing module, a database access module or search function, a dataaggregation module or function, and a classification logic includingcalculation logic to derive a weight or score from aggregated values,and logic for comparing the derived values against a threshold for Spamdetermination.

Processor 206 may have connection by way of a data link 207 to a messagestore 103 for accessing stored messages on behalf of users subscribingto filtering services. A robust user data source 202 may be provided inassociation with email server 201, and in one embodiment containscommunication activity history data for users subscribing to the servicehosted by server 201. In one embodiment, data source 202 may be aseparate data store with server capability tied to server 201 forcommunication. In another embodiment store 202 may be physically builtinto server 201 using added server storage capability.

A data store 204 may in one embodiment be provided within user datasource 202 and can be adapted to hold email account activity historydata typically defined as past email activity for each subscribing useraccount. Data store 204 is typically segregated by user identificationso that email history of a particular user may be classed under thatparticular user's identification. A metadata store 203 may optionally beprovided in one embodiment as a way to optimize access to raw data heldin repository 204. Metadata systems are well known in the art forindexing rich data held in data repositories, such as online datalibraries or other information systems that may be accessed. Data inrepository 203 may typically reference data held in repository 204.

In one embodiment of the invention, instead of a metadata store (203), ahashing function may be used, wherein email addresses are hashed andthen the hash values are matched against a sparsely populated hash tablewhere entries point to data in the historical record associated with aparticular hashed email address. In this embodiment, processor 206 mightbe enabled to hash email addresses found in an email being processedaccording to a same hash formula used in the table.

In one embodiment, although not necessarily required in order topractice filtering methods of the invention, a secondary data store 205might be provided and adapted to store account activity history. In thisoptional embodiment, data store 205 may contain account history definedas past communication history records of other asynchronous orsynchronous communication applications that might be used in networkcommunication. For example, an instant message (IM) application, anon-line chat application, a file sharing application, a white boardcollaboration tool, and other similar applications have activityhistories that may be useful in identifying whether a particular emailmight be legitimate or illegitimate. In this optional embodiment,account history 205 may be represented along with email account history204 in metadata store 203. In one embodiment secondary account historymay also include history related to a second email account that the usermay maintain with another service.

User data 202 may be time constrained to include activity history dataspanning backward for a specified time period, so that history recordsthat have aged past the window limit may be purged from the data source.For example, an exemplary time window to maintain history data for auser might be for a period of 90 days. The time period is arbitrary andmay be set to any logical period of time, which may vary from user touser according to business or personal use concerns, for example.

Server 201, in terms of email activity, typically updates user data 202over a data link 209 from processor 206 on a periodic basis. Update datamay include normal email activity including any changes resulting fromfiltering, which may include new contact additions or deletions from thehistory. Secondary account history, if provided, may be updatedaccording to a subscription model from various account servicefacilities that may have connection to the network. For example, a usermay authorize access to his or her chat transcripts and account datacreated during on-line chat sessions hosted in a chat server connectedto backbone 108. Likewise, peer-to-peer (P2P) activity may also belogged and accessed from the appropriate service provider. A serviceproviding Web-meetings maybe configured to summarize meeting activityand interaction among participants including isolation of the portion ofthe activity involving the user. The data may be updated into store 205immediately after or, in some embodiments during the meeting session. Inone embodiment a user may update repository 205 from station 107, forexample, sending the summarized history of communication activityinvolving a secondary application launched from station 107 if suchactivity has been recorded.

In accordance with one embodiment of the present invention, emailarriving for a user at server 201 may be accessed by filtering processor206 and therefore may be processed against communication historystatistics available from data source 202. In this embodiment, senderidentification, and in some cases, CC and BCC identification expressedas email addresses, may be processed according to pre-conditional statesthat might become apparent from the type of data stored, the manner inwhich it is organized and through analyzing user communication activityhistory. The states or conditions referenced may be used separately orin combination along with a weighting or scoring method in order todetermine a trust metric for a particular message. A value associatedwith the trust metric or metrics may then be used to classify themessage as a legitimate message or as an illegitimate one.

History data does not necessarily have to include all of the dataassociated with the communication activity in order to practiceembodiments of the present invention successfully. Only important anduseful parameters associated with the activity might be extracted fromthe interaction and stored for access. For example parameters like the“from” email address, any CC, BCC email addresses, embedded emailaddresses, subject lines, signatures and/or any other information thatmight lend to successful classification might be considered.

Conditions leading to classification may take shape from the veryexistence of data in one embodiment. The conditions considered for emailclassification may revolve around a central issue defining the nature ofa relationship between a user and a sender of email, as might beexpressed in truths and un-truths. For example, a very basic conditionapplicable to a particular email being processed might be “The User hasaccepted email from the Sender”. Another very basic condition might be“The User has replied to messages from the Sender”. These two conditionsmay be evaluated separately or they may be combined to read “The Userhas accepted mail from the Sender and has replied to the Sender”.

A statistical value may be derived from a single condition or from acompound condition, First the existence of the conditions with respectto account history may be quickly determined, and secondly, the numberof instances of the conditions occurring over the kept history mayattribute a statistical value to the conditions. A relationship betweenuser and sender may be defined according to a hierarchy of possibleconditions until it may be determined that absolutely no relationshipexists at all. The conditions may be thought of as possibilitiessupported by one or more data tuples present in the account history thatmay be tied to some parameter of the email being processed.

Filtering processor 206 in this embodiment may include an ability toparse messages for common contact information that may be defined as asender email address and any other email addresses that might be foundin the CC and BCC fields of the message. In terms of email accounthistory the email addresses extracted from a message by processor 206might or might not appear in a user account activity record. Inaccordance with at least one embodiment of the present invention ameasure of trust is determined for the message by attempting toassociate the message with the record of past activity in some way.

In addition to email addresses, other information may be logged into theaccount records of store 204. For example, if a past message that wasevaluated was found to contain a malicious exe., any email addressesassociated with that message that match any found in the new messagebeing processed might identify a different set of conditions andstatistical values. For example two conditions might be “The User hasreceived a malicious exe. from the Sender” and the condition “The Userand Sender are part of an ongoing communication thread”. These twoconditions seem at face value to oppose each other, however if theconditions are combined and the value associated with the firstcondition is a single instance over a significant history and the valueassociated with the second condition is a high number, then the messagemay be allowed to pass. The determination in the just-mentioned examplemight be that the sender made a forgivable mistake and is notnecessarily a malicious data source.

However, if the value of the first condition in the example just aboveis high (many instances logged), then the system can determine withoutthe user's input that the particular sender is a malicious data sourceor is fast becoming one. In this case, the sender may be marked as aSpam source or as otherwise illegitimate for future correspondence. Thefact that the user was part of an ongoing thread containing frequentmessages from the sender may be mitigated by expunging the senderaddress but not CC or BCC addresses that may still be legitimate assender addresses.

The more complicated second example of classification described aboveillustrates how the system may, in one embodiment, train itself to guardagainst evolving threats. By the same token, if there is absolutely nomatch to a sender address extracted from an email under evaluation thenit does not necessarily mean that the message is illegitimate. Forexample, in this case it might be that a CC address matches an emailaddress from the history record brining up the condition “The User hassent mail to a CC address listed by the Sender” and the condition “TheUser has accepted mail from the CC address listed by the Sender”. Theseconditions alone suggest that the user has some perhaps legitimaterelationship with the sender even though no mail correspondence has evertaken place between the sender and the user. The conditions combined maymake a stronger case for allowing the message through as legitimateemail.

Filtering processor 206 in this embodiment may include a capability forevaluating conditions that are found to exist for a message and acapability for combining the statistical values associated with themessages for the purpose of using the value as a combined weight orscore to classify the messages. As time goes on, the system mayfine-tune itself by taking into account most recent classificationresults, changing the statistical values associated with the possibleconditions defined by the data record. Likewise as a result ofcontinuing activity old data may be purged from the record and new datamay be entered into the record reflecting the natural evolution of emailactivity.

The system of the invention may, in one embodiment, be combined with apermanent blocking list or blacklist technology so that addresses thatcontinually score very low (perhaps definitely malicious) may bepermanently blocked even if natural expulsion of data from the recordtime window erases record of the contact with those addresses.

Considering secondary account history records of data store 205 mayoptionally in one embodiment extend the relationship model. An emailaddress may very often be associated with another form of communicationlike an instant messaging (IM) account for example. Typically a useremail address may be listed in a published user profile and may even beincluded with the user-sent IM messages. If a user has an IM historywith a sender of email that IM history may play a role in determining ameasure of trust that can be attributed to the sender of an emailmessage. Assume, for example, that a message arrives and processor 206determines no match for sender address, CC address, BCC address orembedded email address. In simple terms, the system has never seenanything from that sender in terms of user history.

If, in this example, processor 206 checks data store 205 and determinesthat the sender address is listed as an address associated with an IMactivity record, then the condition “The user has an IM history with thesender” might come into play. The scenario might be that the user hasnever corresponded with the sender using email but the user hascorresponded with the sender using IM and the sender is in fact one ofthe user's IM buddies. The value associated with the indirect (notemail) relationship between the user and the sender may be enough toallow the message through based on a measure of trust found with theexistence of the IM connection. This is especially true if no otherinformation can be determined. However, if the IM condition related tothe above-described embodiment were less robust, like for example, “Thesender has IM'd the user” with lack of reciprocation determined by theabsence of the condition “The user has IM'd the sender”, then the valuestatistic might deter classification of the email message as legitimate.The scenario might have been that the sender is a malicious source thatfirst attempted to IM the user, and, having failed to get a response, issubsequently emailing the user in an attempt to compromise the user insome way.

FIG. 3 is a relationship diagram illustrating an exemplary relationshiphierarchy between a user and a sender in accordance with one embodimentof the present invention. The relationship diagram illustrates one ofmore than one possible user/sender relationship architectures forcreating a relationship model that may be used to filter email.

The central notion of a relationship model is expressed in FIG. 3 inblock form at the root level as User Has Relationship With Sender. Inthis example, the object Relationship can be of Type Direct or of TypeIndirect. According to various embodiments the model of this example maybe created according to different broad rules. In this example directrelationship follows a correspondence-based rule that considers directto include correspondence in any supported media where there may beevidence of acceptance and reply on behalf of a user. Inn this sameexample indirect may be considered to be interaction of a unidirectionalnature in any supported media type. Here is an example, “The User hasreceived a chat invitation from the Sender” and The User has refused achat request from the Sender” (condition set). Or perhaps, “The User hassent email to the Sender” and the absence of the condition “The User hasaccepted email from the Sender”. In the first example of condition setscited immediately above for chat the implication through combining theconditions implies an indirect relationship between user and sender, therelationship initiated by the sender. In the second example for emailthe implication is that the user has an indirect relationship with thesender, the relationship initiated by the user. It may be seen that inthe embodiment described in the above paragraph that the second exampleof email, where the user has initiated an indirect relationship, mightcarry more value then the first example for chat where the sender hasinitiated an indirect relationship. From the perspective of the user,the email of the chat example may likely fail to pass through while theemail related to the indirect email relationship may likely be allowedthrough to the user's inbox.

In this embodiment, if both of the condition sets cited above were foundto be true for an email, then the statistical values might be consultedto determine which response, fail or pass, would be more appropriate.For example if there were a high number of instances of the userrefusing a request for chat from the sender, but only one instance ofthe user sending an email to the sender, then it might be determined toblock the email The implication born out of the statistics might be thatthe user is probably annoyed by the repeated chat requests and has sentan email (a single instance), perhaps to request that the sender stopattempting to chat with him.

On the other hand if the statistics were reversed (high number of emailssent but only a single instance of refusing to accept a chatinvitation), then the implication might be that the user whishes tocorrespond with the sender, but not through a chat interface. In thiscase the email would likely pass through to the user's inbox.

In this example reading the block form as labeled, a Direct RelationshipIs Email or Is one of Media Type IM, Chat, or Collaborate. The objectsIM, chat and collaborate simply list the supported media types andsuggest the possibility that was described further above in accordancewith an embodiment described with reference to FIG. 2 above that theuser has secondary account history activity logged into data store 205which may be used to help determine a classification for emails sent tothe user.

It is noted in this embodiment that the same media objects are alsoillustrated in association with Indirect. A difference with indirectrelationship determination in reference to the possible media types fromdirect relationship determination referencing the same media types issimply the nature of the interactions in the history beingbi-directional for a media type or unidirectional for a media type. Theexistence of data in the history logs for each media type may determineif there is a relationship, whether indirect, direct, or both and forwhich media types the relationship falls under.

In another embodiment model 300 may be created according to anotherbroad consideration. For example, the model may be media based insteadof interaction based. In this case the supported media types IM, chat,and collaborate might not be associated with Direct because onlyactivity found for email would be considered a direct relationship.Likewise, Email might not be associated with Indirect unless it is aseparate email account considered a secondary account history. In theembodiment considering media type as a primary consideration instead ofinteraction type for a relationship definition, email interactionhistory of both bi-directional and unidirectional nature may beconsidered direct in terms of relationship.

A premise for constructing a data model that may be used by thefiltering system may include other considerations in addition to theconsiderations described above, namely interaction-based or media-based,without departing from the spirit and scope of the invention. Theconsiderations described in the embodiments mentioned provide usefulpremise for calculating weights from the conditions found to exist.

It will be apparent to one with skill in the art that in a simpleembodiment in which secondary account activity may not be considered infiltering email, then model 300 might be interaction based and not mediabased where the only account history maintained for a user would be thesubscribed email account history. However, different users may in oneembodiment use different models 300 based on their personal accounthistories, some of which may only include the subscribed-to emailaccount history, and some of which may be extended to include secondaryaccount histories, the model reflecting the media types considered.

FIG. 4 is a table of possible communication activity conditions that mayexist and as might be displayed in a template or list of statistics inaccordance with one embodiment of the present invention. Table 400 inthis example is meant to represent a useable list of possible conditionsthat can be derived from various combinations of data logged in a user'saccount history of activity. This table, in one embodiment, may besimply a visual aid for a programmer. In another embodiment it may be auseable data table of conditions which individually or in combinationmay be caused to execute depending upon the data that may be found in auser's history of interaction that may match contact information from anemail being filtered.

In this embodiment table 400 may include a column 401 listing possibleconditions; a column 402 listing states associated with conditions; anda column 403 listing results, which may be the statistical (Stat) valuesassociated with individual conditions. Table 400 is in this exampleincludes horizontal rows (not numbered) spanning across the columns, therows specific to individual conditions tabled.

Reading generally from top to bottom in table 400 columns 401 in thisexample, a first condition reads, “User has accepted email from Sender”.In the same row, in column 402 the possible state of the condition readsYes/No meaning that the condition is found to exist for an email addressor it is found not to exist. One can also consider a true/falsedesignation instead of a yes/no designation in one embodiment. In thesame row in column 403 a statistical value is displayed indicating avalue that can be applied to weighting or scoring. In one embodiment avalue associated with the condition “User has accepted email fromSender” might simply be the aggregate number of instances that thecondition was found to be true during a time window of activity datarecorded. In another embodiment the value might be a percentage figureexpressing a percentage of the occurrence of the condition in relationto the total number of mails that the user has accepted during aspecified time window.

A next condition in column 401 reads, “User has sent email to Sender”.As described above an indication of YES/NO in column 402 and a value incolumn 403 on the same row gives the state yes it exists or no it doesnot for a given email address, and the statistical value of theoccurrence if it exists. In one embodiment the first two conditions, ifboth true for an email address, may be combined to form one compoundcondition. If a condition is not found then its statistical value may beset to null or 0.

A third condition listed in column 401 reads, “User has replied to emailfrom Sender”. This condition is different from the condition listedimmediately above it, in that replying to a message implies a thread. Ifthe third condition is true then the first two conditions are also trueby definition. However the second condition may be true while the firstand third conditions are false.

A next condition listed in column 401 reads “User is listed as CC bySender”. The next condition reads “The user is listed as BCC by Sender”.Both of these conditions may differ from the first condition in that inone embodiment they may be considered conditions defining an indirectrelationship between user and sender. For example if the definition ofthe first condition specifies that the email is directly addressed tothe user then the fourth or fifth listed condition can be true while thefirst condition is false. In the interest of avoiding redundancy indescription, it will be apparent to the skilled artisan that the variousconditions cited thus far may have relationships to each other and maybe combined in various ways to obtain useful values for obtaining areliable trust metric, which may then be used to classify a message.Other conditions listed in column 401 that have not been cited aresimilarly self-explanatory and one with skill in the art can readilyderive interrelationships between some or many of them and can see withreference to the model 300 described with reference to FIG. 3 above howthose conditions might apply in various embodiments.

Referring now to the last six conditions listed in column 401, eachmarked with an asterisk for isolation purposes, these conditions arethose pertaining to an embodiment where secondary account histories maybe available and considered. In one embodiment it might be that none ofthe conditions related to email are found to exist but that one or moreof the conditions applying to secondary media accounts may be found toexist. In this case the particular email being processed may, in mostcases, still be classified with reliability. As was previously describedfurther above, a sender's email address or other email addresses foundin a particular message being processed may in some instances be tied tosecondary account activity, thus enabling classification, even though noemail interaction history has been recorded between the sender and auser.

FIG. 5 is a flow diagram illustrating basic activities or acts ofprocessor 206 of FIG. 1 according to one embodiment of the presentinvention. In this example at act 501 a candidate email for filteringarrives. In this case there are five email addresses associated with thecandidate email message. For example, in a From field the emailjim@abc.net is displayed representing the sender email address. In a CCfield an email address john@xtz.net and an email address jill@tuv.netare displayed. In a BCC field, the email address jape@123.net and theemail address group@store.biz are displayed.

At act 502, processor 206 (FIG. 1) functions as a feature extractor orparsing module and all five email addresses, in this embodiment, areparsed from the email address fields. An email feature extractor thatcan also find and extract any email addresses that might be additionallyprovided in the email message body may in one embodiment perform theparsing. In this example, there are five addresses to consider, howeverin other cases there may only be one email address for consideration. Instill another embodiment, there may be a quantity limit placed on thenumber of email addresses that can be parsed by default. Such aconsideration might be implemented to protect system integrity in theevent that a malicious sender sends a message loaded with an unusuallyhigh number of email addresses in an attempt to destabilize processingand efficiency.

At act 503 processor 206 functioning as a search utility (databasesearch) accesses historical data associated with the user identified asthe recipient of the email under consideration and looks up anyconditions found to exist or to be true about each email address used inthe lookup. In one embodiment each email address found may be usedcollectively as input in a single access and search function. In anotherembodiment a separate search may be initiated for each email addressused as input. In any instance of act 503 there may be no conditionsfound to exist for one or more addresses input, or there may be someconditions found for one or more addresses used as input. For example,it might be that no conditions exist for jim@abc.net, but one or moreconditions may exist for john@xtz.net and one or more conditions mayexist for group@store.biz exist.

At act 504 in this embodiment processor 206 functions as a dataaggregator and retrieves statistical values for conditions found toexist, aggregating them in one temporary memory location or cacheprovided to processor 206 and adapted for the purpose. At act 505,processor 206 may perform a calculation involving the aggregated valuesto derive a single value that may be compared against a threshold inorder to classify the message being processed. In one embodiment thecalculation may be driven by algorithm and may create a mean or averagevalue from the aggregated values. In another embodiment the aggregatedvalues may be combined according to conditions that may logically becombined, first combining the values for each condition used incombination into a single value for each combination and thencalculating a single value from the combination values.

In still another embodiment, depending on the conditions that exist,single values for single conditions may be discarded if a combination oftwo or more existing conditions can be created, and if so, the newcombined set of conditions may be given a new value assigned to thatspecific combination class. In a further embodiment, a combination ofcalculations may be optionally selected, depending on the number ofconditions that exist and on the prospect of being able to combine twoor more of them. It is likely in some cases that no conditions may befound to exist for a particular message. An optional embodimentdescribed further above addresses this possibility by enabling a searchof secondary account histories using the email address or addressesfound as input.

FIG. 6 is a process flow chart illustrating acts involved in messagefiltering including data updating in accordance with one embodiment ofthe present invention. The process described in this embodiment may ormay not be performed by a single processor with reference to processor206 described further above. The acts included herein may be performedby a combination of hardware and software implements distributedstrategically within an email server system adapted for filteringaccording to an embodiment of the invention.

At act 601 emails arrive within an email server for processing. Thisstep is analogous to step 501 described with reference to FIG. 5. At act601, sender, CC, and BCC email addresses are extracted or parsed. Atstep 602 a data store analogous to store 202 of FIG. 2 is accessed onbehalf of a client. In one embodiment email being stored for manysubscribed clients may be filtered using parallel processes. That is tosay that the process described herein may run in parallel with likemultiple instances filtering emails many client simultaneously.

At act 603 relevant data results (values attributed to found conditions)are retrieved and stored. At act 604 calculation of one or more weightsor scores may be undertaken for each message of each client. At act 607each message being processed may be classified according to the derivedvalue from the message. If the value falls above a threshold then themessage may be classified as legitimate; if below, a threshold, then noaction would likely be taken.

At act 608 pertinent results from new emails having been classified asillegitimate or Spam may be used as update data to a historical datastore like data store 202 referenced in the description referencing FIG.2 above.

FIG. 7 is a block diagram illustrating an associative comparison of useraccounts maintained in a data store for any commonality according to oneembodiment of the present invention. In one embodiment of the inventiondata common to different users may be leveraged to make a classificationin the event that a particular email being processed for a particularuser has no hits associated with it. Data from an account history datastore analogous in some embodiments to data store 202 described withreference to FIG. 2 is illustrated logically as a data table in thisexample to show account history of more than one user subscribed to thesystem of the present invention. There are three illustrated columns ofdata belonging to a User 1, a User 2, and a User n. In actual practicethere will likely be many more subscribed users for which accounthistories may be maintained. The inventor deems that this illustrationof the logical histories of three such users is sufficient forexplanation of this particular embodiment of the invention.

Data store 202 has rows in this example that indicate exemplary datatypes for which history records are kept for each user identified in thecolumns labeled User 1, User 2, and User 3. Reading generally from leftto right and top to bottom in the table, a first column labeled DataType lists types of interaction records that are maintained and that canbe associated with conditions. In a first row, sent email records areillustrated for each of users 1-n. In this row and for each column a“To” list, and a “CC/BCC” list may be maintained. The “To” list may beadapted to identify email addresses that each user has sent mail to. The“CC/BCC” list identifies email addresses that the user has referencedwhen the emails of record were sent to the addresses in the “To” list.

A next row identifies received email records for each user identified incolumns User 1-n. Received email records may contain a “From” list and a“CC/BCC” list. The from list identifies email addresses that are senderaddresses of email sent to a user. The “CC/BCC” list for this rowidentifies email addresses referenced by the sender in emails of recordsent to the user.

A third row identifies reply mail records. This row may contain adirectory or list of email addresses from senders that the user hasreceived email from and has replied to. Reply records might also containa CC/BCC list, although none is illustrated here. In this simplifiedexample, assume now that an email arrives for user 1 fromuser_n@xyz.net. In this case, user n like user 1 is a subscriber to thefiltering service of the invention. The account history data for user nis maintained in the data store as is the data for user 1. However, inthis case there are no CC/BCC addresses in the message only the senderaddress use_n@xyz.net.

In this example, assume that user 1 has no record of any interactionwith user n and relying solely on the records of user 1, the messagecannot be classified. In this embodiment however the message may beclassified because user n is a subscriber to the same email server asuser 1, therefore account history for both accounts can be searched forconditions that might exist that can tie the two together in some way.In this case the commonality between user n and user 1 is that they bothhave had some relationship with a joe@abc.com. For example, user n hassent mail to joe@abc.com and has received mail from a sender wherejoe@abc.com was listed as a CC or BCC. User 1 has also sent mail tojoe@abc.com and has accepted mail from joe@abc.com.

A bottom row labeled Reference List contains reference email addressesthat are common to two or more subscribers whose account history data ismaintained. The message from user n to user 1 can be classified based onconditions that exist for both user 1 and user n referencing a commonemail address of record. The implication might be that both parties, thesender and recipient, trust Joe, so there is a likelihood that they cantrust each other.

The data and data arrangements and included data logically representedin this embodiment are exemplary of only one possible instance where amessage may be classified based on cross-referencing account historydata between users. Conditions, values, additional email record data andso on that might be included in a variety of arrangements (rows, andcolumns) or additional data types maintained are not illustrated herebut may be assumed to be present in various embodiments.

In one embodiment processor 206 could track the magnitude of incomingemail from servers and networks and analyze various properties todetermine a value associated with the trust metric or metrics used toclassify the message as a legitimate message or an illegitimate message.In particular, in one embodiment processor 206 could identify a messagetransmission path and filter mail based on whether the transmission pathwhose providence is known or unknown.

One with skill in the art of whitelisting technologies will appreciatethat the methods of the invention as described in various embodiments inthis specification are much more flexible both in deterring threats andaccepting new contacts than are conventional whitelisting methods in theart. The user, in one embodiment, may not have to maintain a whitelistof any kind and in still another embodiment may even rely on the systemto maintain his or her email address books. For example, when a newlegitimate contact is accepted the system may alert the user to thepossibility of adding the contact to a permanent list. Likewise, if atrusted contact is determined to become an un trusted contact due torecent evaluation, then the system, in one embodiment, may erasereferences to the contact in the user contact address records.

In one embodiment of the invention processor 206 may be separated intodifferent functions which may be provided as an array of independentsoftware components that may not necessarily reside together on a sameprocessing component. For example, the processes that may be initiatedas standalone applications might include parsing or feature extraction,search or data access, data aggregation, value calculation, valuecomparison against a standard threshold, message tagging, and dataupdating, among others.

It has occurred to the present inventor that the probability that anemail or a group of emails is spam may be reckoned, at least to someextent, by the nature of traffic on the Internet from certain sources orgroups of sources. In one embodiment of the present invention,therefore, certain traffic statistics and characteristics may be takeninto account to determine the trustworthiness of emails.

FIG. 8 is a table of possible communication network traffic activityproperties that may exist and as might be displayed in a template orlist of statistics for use in accordance with one embodiment of thepresent invention. Table 800 in this example is meant to represent auseable list of possible conditions that can be derived by monitoringnetwork email traffic. Table 800, in one embodiment, may be associatedwith a suspected source of spam, may be associated with a group ofsources or purported sources suspected of sending spam emails, or maysimply be developed at random for a source or group of sources, and usedto develop a trust metric. The trust metric, of course, may indicate ahigh level of trust, or, on the other end of the scale, may indicate acertainty that the source is a sender of spam emails.

There are a variety of ways such a collection of information may beused. The results may, for example, be input as a visual aid for aprogrammer. In some instances tables may be developed by monitoringspecific sources and groups of sources and used to trigger automaticfunctions to automatically treat emails from the sources, the functionsranging from labeling emails with a probability of trust, divertingemails for further testing before delivery, or even destroying emailsoutright as certain spam.

In one embodiment table 800 may include a column 801 listing possibleproperties; a column 802 listing a numeric value associated withproperties; and a column 803 listing results, which may be thestatistical (Stat) values associated with individual properties. Table800 in this example includes horizontal rows 805-809 for each of aspectrum of properties. It is emphasized again that the table and theproperties listed are exemplary, and there may be other useful trafficproperties that may be combined with those described below withreference to FIG. 8, or that may be used instead of those described.

Row 805 in this example is for “Traffic volume”. Column 802 for trafficvolume is in this example a numerical value for traffic volume measuredin a specific time period. This value may be a snapshot of volume at anypoint in time, for example, as emails per minute, of a number of emailsdetected from a source or source group over some other time period. Thisnumber represents the momentary traffic volume arriving at email server201 (FIG. 2).

Column 803 for traffic volume is a statistical value (Stat value), whichmay be a normalized or canonicalized value derived from the value incolumn 802. The column 803 value may be, for example, a numerical valuefrom 1 to 10 indicating a relative traffic volume over time normalizedto know spam volumes, and so on. There are a broad variety ofpossibilities.

Row 806 is for a property related to burstiness. Burstiness may bedefined as the way that email volume relative to time may vary. Forexample, if traffic does not vary over time the value for burstinesswould be very low. On the other hand, if there are periods of noactivity, and intervening periods of high traffic volume, burstinessvalue would be high. The value in column 803 for burstiness may then bea normalized or canonicalized value for burstiness according to knownburstiness characteristics for known spam sources and known trustedsources.

Row 807 is for “Number of recipients in this example. In one embodimentthis property could reflect the total number of recipients to whom aparticular email or group of emails is addressed; in another embodimentthis property could reflect the number of recipients to receive thismail on email server 201. Again, column 802 may be a specific value, andcolumn 803 may be a normalized value based on known or theorizedcharacteristics for such a property, related to known spam sources ortrusted sources.

It is known that spammers often counterfeit sender identification tocreate a greater trust metric for their emails. Spammers may also, in asingle campaign, use a multiplicity of counterfeit senders. Therefore, ametric relative to senders and number of purported senders may be usefulin establishing a probability that an email or a group or burst ofemails is spam. In FIG. 8 line 808 is for a metric labeled “Number ofpurported senders”. Columns 802 and 803 may then list an actual valuefor a source or a group of sources, and column 803 a value based on thevalue of 802 reflecting known spam and/or trusted sources.

Row 809 in this example is for “Mail recipient connection Type”. Column802 for this line in the example table reflects whether the mailrecipient is on a dynamic or static network IP address connection. Amail recipient with a static IP address will retain the same IP addresseach time they are connected to the internet; a mail recipient with adynamic IP address may or may not have the same address each time theyare connected to the internet. A Column 803 in this example couldcontain a metric reflecting the trustworthiness of the sender of themail based on whether the recipient is using a dynamic or static IPaddress to connect to email server 201.

Row 804 of the exemplary table of FIG. 8 is for a hash total, or otherderived stat value based on the normalized or canonocalized values forsome or all of the properties in rows 805 through 809. Depending uponthe relationship basis used to develop the values, the final derivedvalue could be as simple as a total of the stat values in column 803 forrows 805 through 809. Many other relationships might be used as well.The final value is to be a value that can be used to reflect theprobability that emails from real or purported sources may or may not bespam.

The final value derived for row 804 may be used in a variety of ways.The value may be an indicator for an administrator or other worker tocombine with personal judgment in making decisions regarding emails andemail campaigns. In other cases, in a more automated environment, thefinal derived value may be used to trigger one or more automatedfunctions as previously described above. For example, in one embodiment,it may have been empirically developed that a number greater than acertain threshold is a solid indication that the emails monitored arespam and a function may be triggered to simply eradicate all the emailsin the known campaign.

It will be appreciated by those skilled in the art that the invention isnot limited to the exemplary embodiments described, and may beimplemented with some, or a combination of the described or otherfeatures without departing from the spirit and scope of the invention,and many details of embodiments described may be altered appreciablyalso without departing from the spirit and scope of the invention. Theinvention is to be limited therefore only to the scope of the followingclaims.

1. (canceled)
 2. A computer implemented method for filtering emails on aserver system having one or more processors and memory storing one ormore programs for execution by the one or more processors, comprising:receiving an email to a recipient from a sender; identifying a spamprobability of the email based at least in part on historicalinteractions between the recipient and the sender, wherein thehistorical interactions include interactions from an email applicationand one or more non-email applications including at least one of: afile-sharing application and a collaboration application; andclassifying the email according the identified spam probability.
 3. Thecomputer implemented method of claim 2, wherein identifying the spamprobability further comprises: combining historical interactions fromthe email application and historical interactions from the one or morenon-email applications to produce a combined condition; and identifyingthe spam probability of the email based at least in part on the combinedcondition.
 4. The computer implemented method of claim 1, whereinidentifying the spam probability further comprises: identifying the spamprobability of the email based on historical interactions between one ormore additional recipients and the sender.
 5. The computer implementedmethod of claim 1, wherein the one or more non-email applicationsincludes a plurality of: an instant messaging application, an on-linechat application, a file-sharing application, and a collaborationapplication.
 6. The computer implemented method of claim 1, whereinidentifying the spam probability further comprises: identifying a spamprobability of the email based at least in part on statisticalinformation relating to previous interactions between the recipient andthe sender, wherein the statistical information relating to previousinteractions includes one or more of the following: types ofinteraction, media of interaction, and frequency of interaction.
 7. Asystem, for filtering emails, comprising: at least one processor; andmemory storing one or more programs to be executed by the at least oneprocessor; the one or more programs comprising instructions for:receiving an email to a recipient from a sender; identifying a spamprobability of the email based at least in part on historicalinteractions between the recipient and the sender, wherein thehistorical interactions include interactions from an email applicationand one or more non-email applications including at least one of: afile-sharing application and a collaboration application; andclassifying the email according the identified spam probability.
 8. Thesystem of claim 7, wherein instructions for identifying the spamprobability further comprises instructions for: combining historicalinteractions from the email application and historical interactions fromthe one or more non-email applications to produce a combined condition;and identifying the spam probability of the email based at least in parton the combined condition.
 9. The system of claim 7, whereininstructions for identifying the spam probability further comprisesinstructions for: identifying the spam probability of the email based onhistorical interactions between one or more additional recipients andthe sender.
 10. The system of claim 7, wherein the one or more non-emailapplications also includes a plurality of: an instant messagingapplication, an on-line chat application, a file-sharing application,and a collaboration application.
 11. The system of claim 7, whereininstructions for identifying the spam probability further comprisesinstructions for: identifying a spam probability of the email based atleast in part on statistical information relating to previousinteractions between the recipient and the sender, wherein thestatistical information relating to previous interactions includes oneor more of the following: types of interaction, media of interaction,and frequency of interaction.
 12. A computer readable storage mediumstoring one or more programs configured for execution by a computer, theone or more programs comprising instructions for: receiving an email toa recipient from a sender; identifying a spam probability of the emailbased at least in part on historical interactions between the recipientand the sender, wherein the historical interactions include interactionsfrom an email application and one or more non-email applicationsincluding at least one of: a file-sharing application and acollaboration application; and classifying the email according theidentified spam probability.
 13. The computer readable storage medium ofclaim 12, wherein instructions for identifying the spam probabilityfurther comprises instructions for: combining historical interactionsfrom the email application and historical interactions from the one ormore non-email applications to produce a combined condition; andidentifying the spam probability of the email based at least in part onthe combined condition.
 14. The computer readable storage medium ofclaim 12, wherein instructions for identifying the spam probabilityfurther comprises instructions for: identifying the spam probability ofthe email based on historical interactions between one or moreadditional recipients and the sender.
 15. The computer readable storagemedium of claim 12, wherein the one or more non-email applications alsoincludes a plurality of: an instant messaging application, an on-linechat application, a file-sharing application, and a collaborationapplication.
 16. The computer readable storage medium of claim 12,wherein instructions for identifying the spam probability furthercomprises instructions for: identifying a spam probability of the emailbased at least in part on statistical information relating to previousinteractions between the recipient and the sender, wherein thestatistical information relating to previous interactions includes oneor more of the following: types of interaction, media of interaction,and frequency of interaction.
 17. A computer implemented method forfiltering emails on a server system having one or more processors andmemory storing one or more programs for execution by the one or moreprocessors, comprising: receiving an email to a recipient from a sender;tracking network traffic burstiness from the network associated with thesender, wherein network traffic burstiness corresponds to variations inemail volume relative to time for the network; identifying a burstinessspam probability of the email based by comparing the network trafficburstiness to the burstiness of known trusted sources and the burstinessof known spam sources; and classifying the email at least in part basedon the burstiness spam probability.
 18. The computer implemented methodof claim 17, further comprising: tracking at least one additionalnetwork characteristic for the network associated with the sender, theadditional tracked network characteristic selected from the groupconsisting of: network traffic volume per unit of time, number ofrecipients, number of purported senders, and mail recipient connectiontype; comparing the additional tracked network characteristic for theemail to known values for email from trusted or untrusted sources; andclassifying the email at least in part based on the compared additionalnetwork characteristic.
 19. The computer implemented method of claim 18,further comprising: combining network traffic burstiness with the atleast one additional network characteristic, to produce a combinedtracked rating value; and classifying the email at least in part basedon the combined tracked rating value.
 20. A system, for filteringemails, comprising: at least one processor; and memory storing one ormore programs to be executed by the at least one processor; the one ormore programs comprising instructions for: receiving an email to arecipient from a sender; tracking network traffic burstiness from thenetwork associated with the sender, wherein network traffic burstinesscorresponds to variations in email volume relative to time for thenetwork; identifying a burstiness spam probability of the email based bycomparing the network traffic burstiness to the burstiness of knowntrusted sources and the burstiness of known spam sources; andclassifying the email at least in part based on the burstiness spamprobability.
 21. The system of claim 20, further comprising instructionsfor: tracking at least one additional network characteristic for networkassociated with the sender, the additional tracked networkcharacteristic selected from the group consisting of: network trafficvolume per unit of time, number of recipients, number of purportedsenders, and mail recipient connection type; comparing the additionaltracked network characteristic for the email to known values for emailfrom trusted or untrusted sources; and classifying the email at least inpart based on the compared additional network characteristic.
 22. Thesystem of claim 21, further comprising instructions for: combiningnetwork traffic burstiness with the at least one additional networkcharacteristic, to produce a combined tracked rating value; andclassifying the email at least in part based on the combined trackedrating value.
 23. A computer readable storage medium storing one or moreprograms configured for execution by a computer, the one or moreprograms comprising instructions for: receiving an email to a recipientfrom a sender; tracking network traffic burstiness from the networkassociated with the sender, wherein network traffic burstinesscorresponds to variations in email volume relative to time for thenetwork; identifying a burstiness spam probability of the email based bycomparing the network traffic burstiness to the burstiness of knowntrusted sources and the burstiness of known spam sources; andclassifying the email at least in part based on the burstiness spamprobability.
 24. The computer readable storage medium of claim 23,further comprising instructions for: tracking at least one additionalnetwork characteristic for the network associated with the sender, theadditional tracked network characteristic selected from the groupconsisting of: network traffic volume per unit of time, number ofrecipients, number of purported senders, and mail recipient connectiontype; comparing the additional tracked network characteristic for theemail to known values for email from trusted or untrusted sources; andclassifying the email at least in part based on the compared additionalnetwork characteristic.
 25. The computer readable storage medium ofclaim 24, further comprising instructions for: combining network trafficburstiness with the at least one additional network characteristic, toproduce a combined tracked rating value; and classifying the email atleast in part based on the combined tracked rating value.